Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory208.0 B

Variable types

Numeric9
Categorical11
Boolean5
Text1

Alerts

Exercise Induced Angina is highly imbalanced (53.5%) Imbalance
Screen Time (hrs/day) has 664 (6.6%) zeros Zeros

Reproduction

Analysis started2025-02-14 11:11:35.017938
Analysis finished2025-02-14 11:11:57.941941
Duration22.92 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.6015
Minimum18
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:11:58.114250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q122
median27
Q331
95-th percentile35
Maximum35
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.164239
Coefficient of variation (CV)0.19413338
Kurtosis-1.1983982
Mean26.6015
Median Absolute Deviation (MAD)4
Skewness-0.014238085
Sum266015
Variance26.669365
MonotonicityNot monotonic
2025-02-14T11:11:58.323102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
32 597
 
6.0%
26 583
 
5.8%
33 577
 
5.8%
27 575
 
5.8%
35 569
 
5.7%
24 568
 
5.7%
23 565
 
5.7%
34 561
 
5.6%
22 559
 
5.6%
28 556
 
5.6%
Other values (8) 4290
42.9%
ValueCountFrequency (%)
18 520
5.2%
19 523
5.2%
20 550
5.5%
21 528
5.3%
22 559
5.6%
23 565
5.7%
24 568
5.7%
25 550
5.5%
26 583
5.8%
27 575
5.8%
ValueCountFrequency (%)
35 569
5.7%
34 561
5.6%
33 577
5.8%
32 597
6.0%
31 533
5.3%
30 553
5.5%
29 533
5.3%
28 556
5.6%
27 575
5.8%
26 583
5.8%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Male
4913 
Female
4856 
Other
 
231

Length

Max length6
Median length5
Mean length4.9943
Min length4

Characters and Unicode

Total characters49943
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 4913
49.1%
Female 4856
48.6%
Other 231
 
2.3%

Length

2025-02-14T11:11:58.717355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:11:58.837089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 4913
49.1%
female 4856
48.6%
other 231
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 14856
29.7%
a 9769
19.6%
l 9769
19.6%
M 4913
 
9.8%
F 4856
 
9.7%
m 4856
 
9.7%
O 231
 
0.5%
t 231
 
0.5%
h 231
 
0.5%
r 231
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39943
80.0%
Uppercase Letter 10000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14856
37.2%
a 9769
24.5%
l 9769
24.5%
m 4856
 
12.2%
t 231
 
0.6%
h 231
 
0.6%
r 231
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
M 4913
49.1%
F 4856
48.6%
O 231
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 49943
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14856
29.7%
a 9769
19.6%
l 9769
19.6%
M 4913
 
9.8%
F 4856
 
9.7%
m 4856
 
9.7%
O 231
 
0.5%
t 231
 
0.5%
h 231
 
0.5%
r 231
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14856
29.7%
a 9769
19.6%
l 9769
19.6%
M 4913
 
9.8%
F 4856
 
9.7%
m 4856
 
9.7%
O 231
 
0.5%
t 231
 
0.5%
h 231
 
0.5%
r 231
 
0.5%

Region
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Central
1746 
North
1700 
South
1664 
East
1658 
West
1643 

Length

Max length10
Median length7
Mean length5.8136
Min length4

Characters and Unicode

Total characters58136
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowEast
3rd rowNorth
4th rowEast
5th rowWest

Common Values

ValueCountFrequency (%)
Central 1746
17.5%
North 1700
17.0%
South 1664
16.6%
East 1658
16.6%
West 1643
16.4%
North-East 1589
15.9%

Length

2025-02-14T11:11:58.976554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:11:59.136211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
central 1746
17.5%
north 1700
17.0%
south 1664
16.6%
east 1658
16.6%
west 1643
16.4%
north-east 1589
15.9%

Most occurring characters

ValueCountFrequency (%)
t 11589
19.9%
r 5035
8.7%
a 4993
8.6%
o 4953
8.5%
h 4953
8.5%
s 4890
8.4%
e 3389
 
5.8%
N 3289
 
5.7%
E 3247
 
5.6%
C 1746
 
3.0%
Other values (6) 10052
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44958
77.3%
Uppercase Letter 11589
 
19.9%
Dash Punctuation 1589
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 11589
25.8%
r 5035
11.2%
a 4993
11.1%
o 4953
11.0%
h 4953
11.0%
s 4890
10.9%
e 3389
 
7.5%
n 1746
 
3.9%
l 1746
 
3.9%
u 1664
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
N 3289
28.4%
E 3247
28.0%
C 1746
15.1%
S 1664
14.4%
W 1643
14.2%
Dash Punctuation
ValueCountFrequency (%)
- 1589
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56547
97.3%
Common 1589
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 11589
20.5%
r 5035
8.9%
a 4993
8.8%
o 4953
8.8%
h 4953
8.8%
s 4890
8.6%
e 3389
 
6.0%
N 3289
 
5.8%
E 3247
 
5.7%
C 1746
 
3.1%
Other values (5) 8463
15.0%
Common
ValueCountFrequency (%)
- 1589
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 11589
19.9%
r 5035
8.7%
a 4993
8.6%
o 4953
8.5%
h 4953
8.5%
s 4890
8.4%
e 3389
 
5.8%
N 3289
 
5.7%
E 3247
 
5.6%
C 1746
 
3.0%
Other values (6) 10052
17.3%

Urban/Rural
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Urban
5918 
Rural
4082 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters50000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowRural

Common Values

ValueCountFrequency (%)
Urban 5918
59.2%
Rural 4082
40.8%

Length

2025-02-14T11:11:59.317728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:11:59.412562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urban 5918
59.2%
rural 4082
40.8%

Most occurring characters

ValueCountFrequency (%)
r 10000
20.0%
a 10000
20.0%
U 5918
11.8%
b 5918
11.8%
n 5918
11.8%
R 4082
8.2%
u 4082
8.2%
l 4082
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40000
80.0%
Uppercase Letter 10000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10000
25.0%
a 10000
25.0%
b 5918
14.8%
n 5918
14.8%
u 4082
10.2%
l 4082
10.2%
Uppercase Letter
ValueCountFrequency (%)
U 5918
59.2%
R 4082
40.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 50000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10000
20.0%
a 10000
20.0%
U 5918
11.8%
b 5918
11.8%
n 5918
11.8%
R 4082
8.2%
u 4082
8.2%
l 4082
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10000
20.0%
a 10000
20.0%
U 5918
11.8%
b 5918
11.8%
n 5918
11.8%
R 4082
8.2%
u 4082
8.2%
l 4082
8.2%

SES
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Low
4019 
Middle
3942 
High
2039 

Length

Max length6
Median length4
Mean length4.3865
Min length3

Characters and Unicode

Total characters43865
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiddle
2nd rowLow
3rd rowLow
4th rowMiddle
5th rowLow

Common Values

ValueCountFrequency (%)
Low 4019
40.2%
Middle 3942
39.4%
High 2039
20.4%

Length

2025-02-14T11:11:59.560277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:11:59.672127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 4019
40.2%
middle 3942
39.4%
high 2039
20.4%

Most occurring characters

ValueCountFrequency (%)
d 7884
18.0%
i 5981
13.6%
L 4019
9.2%
o 4019
9.2%
w 4019
9.2%
M 3942
9.0%
l 3942
9.0%
e 3942
9.0%
H 2039
 
4.6%
g 2039
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33865
77.2%
Uppercase Letter 10000
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 7884
23.3%
i 5981
17.7%
o 4019
11.9%
w 4019
11.9%
l 3942
11.6%
e 3942
11.6%
g 2039
 
6.0%
h 2039
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
L 4019
40.2%
M 3942
39.4%
H 2039
20.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 43865
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 7884
18.0%
i 5981
13.6%
L 4019
9.2%
o 4019
9.2%
w 4019
9.2%
M 3942
9.0%
l 3942
9.0%
e 3942
9.0%
H 2039
 
4.6%
g 2039
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 7884
18.0%
i 5981
13.6%
L 4019
9.2%
o 4019
9.2%
w 4019
9.2%
M 3942
9.0%
l 3942
9.0%
e 3942
9.0%
H 2039
 
4.6%
g 2039
 
4.6%

Smoking Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Never
5013 
Occasionally
2967 
Regularly
2020 

Length

Max length12
Median length5
Mean length7.8849
Min length5

Characters and Unicode

Total characters78849
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowOccasionally
3rd rowOccasionally
4th rowOccasionally
5th rowOccasionally

Common Values

ValueCountFrequency (%)
Never 5013
50.1%
Occasionally 2967
29.7%
Regularly 2020
20.2%

Length

2025-02-14T11:11:59.814358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:11:59.932038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
never 5013
50.1%
occasionally 2967
29.7%
regularly 2020
20.2%

Most occurring characters

ValueCountFrequency (%)
e 12046
15.3%
l 9974
12.6%
a 7954
10.1%
r 7033
8.9%
c 5934
 
7.5%
N 5013
 
6.4%
v 5013
 
6.4%
y 4987
 
6.3%
O 2967
 
3.8%
s 2967
 
3.8%
Other values (6) 14961
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68849
87.3%
Uppercase Letter 10000
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12046
17.5%
l 9974
14.5%
a 7954
11.6%
r 7033
10.2%
c 5934
8.6%
v 5013
7.3%
y 4987
7.2%
s 2967
 
4.3%
i 2967
 
4.3%
o 2967
 
4.3%
Other values (3) 7007
10.2%
Uppercase Letter
ValueCountFrequency (%)
N 5013
50.1%
O 2967
29.7%
R 2020
20.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 78849
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12046
15.3%
l 9974
12.6%
a 7954
10.1%
r 7033
8.9%
c 5934
 
7.5%
N 5013
 
6.4%
v 5013
 
6.4%
y 4987
 
6.3%
O 2967
 
3.8%
s 2967
 
3.8%
Other values (6) 14961
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12046
15.3%
l 9974
12.6%
a 7954
10.1%
r 7033
8.9%
c 5934
 
7.5%
N 5013
 
6.4%
v 5013
 
6.4%
y 4987
 
6.3%
O 2967
 
3.8%
s 2967
 
3.8%
Other values (6) 14961
19.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Never
6006 
Occasionally
2999 
Regularly
995 

Length

Max length12
Median length5
Mean length7.4973
Min length5

Characters and Unicode

Total characters74973
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegularly
2nd rowOccasionally
3rd rowOccasionally
4th rowNever
5th rowOccasionally

Common Values

ValueCountFrequency (%)
Never 6006
60.1%
Occasionally 2999
30.0%
Regularly 995
 
10.0%

Length

2025-02-14T11:12:00.088294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:00.202431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
never 6006
60.1%
occasionally 2999
30.0%
regularly 995
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 13007
17.3%
l 7988
10.7%
r 7001
9.3%
a 6993
9.3%
N 6006
8.0%
v 6006
8.0%
c 5998
8.0%
y 3994
 
5.3%
O 2999
 
4.0%
s 2999
 
4.0%
Other values (6) 11982
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64973
86.7%
Uppercase Letter 10000
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13007
20.0%
l 7988
12.3%
r 7001
10.8%
a 6993
10.8%
v 6006
9.2%
c 5998
9.2%
y 3994
 
6.1%
s 2999
 
4.6%
i 2999
 
4.6%
o 2999
 
4.6%
Other values (3) 4989
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
N 6006
60.1%
O 2999
30.0%
R 995
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74973
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13007
17.3%
l 7988
10.7%
r 7001
9.3%
a 6993
9.3%
N 6006
8.0%
v 6006
8.0%
c 5998
8.0%
y 3994
 
5.3%
O 2999
 
4.0%
s 2999
 
4.0%
Other values (6) 11982
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13007
17.3%
l 7988
10.7%
r 7001
9.3%
a 6993
9.3%
N 6006
8.0%
v 6006
8.0%
c 5998
8.0%
y 3994
 
5.3%
O 2999
 
4.0%
s 2999
 
4.0%
Other values (6) 11982
16.0%

Diet Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Non-Vegetarian
4943 
Vegetarian
4057 
Vegan
1000 

Length

Max length14
Median length10
Mean length11.4772
Min length5

Characters and Unicode

Total characters114772
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-Vegetarian
2nd rowNon-Vegetarian
3rd rowVegan
4th rowVegetarian
5th rowVegetarian

Common Values

ValueCountFrequency (%)
Non-Vegetarian 4943
49.4%
Vegetarian 4057
40.6%
Vegan 1000
 
10.0%

Length

2025-02-14T11:12:00.342700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:00.452232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
non-vegetarian 4943
49.4%
vegetarian 4057
40.6%
vegan 1000
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 19000
16.6%
a 19000
16.6%
n 14943
13.0%
V 10000
8.7%
g 10000
8.7%
t 9000
7.8%
r 9000
7.8%
i 9000
7.8%
N 4943
 
4.3%
o 4943
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94886
82.7%
Uppercase Letter 14943
 
13.0%
Dash Punctuation 4943
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19000
20.0%
a 19000
20.0%
n 14943
15.7%
g 10000
10.5%
t 9000
9.5%
r 9000
9.5%
i 9000
9.5%
o 4943
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
V 10000
66.9%
N 4943
33.1%
Dash Punctuation
ValueCountFrequency (%)
- 4943
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109829
95.7%
Common 4943
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19000
17.3%
a 19000
17.3%
n 14943
13.6%
V 10000
9.1%
g 10000
9.1%
t 9000
8.2%
r 9000
8.2%
i 9000
8.2%
N 4943
 
4.5%
o 4943
 
4.5%
Common
ValueCountFrequency (%)
- 4943
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 19000
16.6%
a 19000
16.6%
n 14943
13.0%
V 10000
8.7%
g 10000
8.7%
t 9000
7.8%
r 9000
7.8%
i 9000
7.8%
N 4943
 
4.3%
o 4943
 
4.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Sedentary
4954 
Moderate
4023 
High
1023 

Length

Max length9
Median length8
Mean length8.0862
Min length4

Characters and Unicode

Total characters80862
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSedentary
2nd rowSedentary
3rd rowHigh
4th rowSedentary
5th rowModerate

Common Values

ValueCountFrequency (%)
Sedentary 4954
49.5%
Moderate 4023
40.2%
High 1023
 
10.2%

Length

2025-02-14T11:12:00.620234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:00.739810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sedentary 4954
49.5%
moderate 4023
40.2%
high 1023
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 17954
22.2%
d 8977
11.1%
t 8977
11.1%
a 8977
11.1%
r 8977
11.1%
S 4954
 
6.1%
n 4954
 
6.1%
y 4954
 
6.1%
M 4023
 
5.0%
o 4023
 
5.0%
Other values (4) 4092
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70862
87.6%
Uppercase Letter 10000
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17954
25.3%
d 8977
12.7%
t 8977
12.7%
a 8977
12.7%
r 8977
12.7%
n 4954
 
7.0%
y 4954
 
7.0%
o 4023
 
5.7%
i 1023
 
1.4%
g 1023
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
S 4954
49.5%
M 4023
40.2%
H 1023
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 80862
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17954
22.2%
d 8977
11.1%
t 8977
11.1%
a 8977
11.1%
r 8977
11.1%
S 4954
 
6.1%
n 4954
 
6.1%
y 4954
 
6.1%
M 4023
 
5.0%
o 4023
 
5.0%
Other values (4) 4092
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17954
22.2%
d 8977
11.1%
t 8977
11.1%
a 8977
11.1%
r 8977
11.1%
S 4954
 
6.1%
n 4954
 
6.1%
y 4954
 
6.1%
M 4023
 
5.0%
o 4023
 
5.0%
Other values (4) 4092
 
5.1%

Screen Time (hrs/day)
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5107
Minimum0
Maximum15
Zeros664
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:00.854124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6265782
Coefficient of variation (CV)0.61599827
Kurtosis-1.2182299
Mean7.5107
Median Absolute Deviation (MAD)4
Skewness-0.011736638
Sum75107
Variance21.405226
MonotonicityNot monotonic
2025-02-14T11:12:00.996763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 664
 
6.6%
4 653
 
6.5%
12 653
 
6.5%
8 652
 
6.5%
14 645
 
6.5%
13 638
 
6.4%
9 635
 
6.3%
5 632
 
6.3%
11 628
 
6.3%
3 613
 
6.1%
Other values (6) 3587
35.9%
ValueCountFrequency (%)
0 664
6.6%
1 604
6.0%
2 607
6.1%
3 613
6.1%
4 653
6.5%
5 632
6.3%
6 583
5.8%
7 598
6.0%
8 652
6.5%
9 635
6.3%
ValueCountFrequency (%)
15 609
6.1%
14 645
6.5%
13 638
6.4%
12 653
6.5%
11 628
6.3%
10 586
5.9%
9 635
6.3%
8 652
6.5%
7 598
6.0%
6 583
5.8%

Sleep Duration (hrs/day)
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4896
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:01.138185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median6
Q38
95-th percentile10
Maximum10
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2893265
Coefficient of variation (CV)0.35276851
Kurtosis-1.2338383
Mean6.4896
Median Absolute Deviation (MAD)2
Skewness0.013889728
Sum64896
Variance5.2410159
MonotonicityNot monotonic
2025-02-14T11:12:01.270650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 1276
12.8%
8 1274
12.7%
10 1268
12.7%
5 1263
12.6%
6 1260
12.6%
3 1236
12.4%
7 1235
12.3%
9 1188
11.9%
ValueCountFrequency (%)
3 1236
12.4%
4 1276
12.8%
5 1263
12.6%
6 1260
12.6%
7 1235
12.3%
8 1274
12.7%
9 1188
11.9%
10 1268
12.7%
ValueCountFrequency (%)
10 1268
12.7%
9 1188
11.9%
8 1274
12.7%
7 1235
12.3%
6 1260
12.6%
5 1263
12.6%
4 1276
12.8%
3 1236
12.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7071 
True
2929 
ValueCountFrequency (%)
False 7071
70.7%
True 2929
29.3%
2025-02-14T11:12:01.380030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Diabetes
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
8002 
True
1998 
ValueCountFrequency (%)
False 8002
80.0%
True 1998
 
20.0%
2025-02-14T11:12:01.451510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7548 
True
2452 
ValueCountFrequency (%)
False 7548
75.5%
True 2452
 
24.5%
2025-02-14T11:12:01.524584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct201
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.649
Minimum100
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:01.692119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile110
Q1150
median199
Q3249
95-th percentile289
Maximum300
Range200
Interquartile range (IQR)99

Descriptive statistics

Standard deviation57.561902
Coefficient of variation (CV)0.2883155
Kurtosis-1.193264
Mean199.649
Median Absolute Deviation (MAD)50
Skewness-0.00062048855
Sum1996490
Variance3313.3725
MonotonicityNot monotonic
2025-02-14T11:12:01.902714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227 73
 
0.7%
148 63
 
0.6%
185 63
 
0.6%
237 63
 
0.6%
106 63
 
0.6%
138 63
 
0.6%
173 63
 
0.6%
275 62
 
0.6%
119 62
 
0.6%
225 61
 
0.6%
Other values (191) 9364
93.6%
ValueCountFrequency (%)
100 60
0.6%
101 48
0.5%
102 33
0.3%
103 44
0.4%
104 44
0.4%
105 51
0.5%
106 63
0.6%
107 54
0.5%
108 46
0.5%
109 48
0.5%
ValueCountFrequency (%)
300 45
0.4%
299 47
0.5%
298 31
0.3%
297 43
0.4%
296 47
0.5%
295 48
0.5%
294 55
0.5%
293 33
0.3%
292 52
0.5%
291 43
0.4%

BMI (kg/m²)
Real number (ℝ)

Distinct251
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.4419
Minimum15
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:02.134728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.2
Q121.2
median27.5
Q333.7
95-th percentile38.8
Maximum40
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.2237401
Coefficient of variation (CV)0.26323761
Kurtosis-1.1980763
Mean27.4419
Median Absolute Deviation (MAD)6.2
Skewness0.0094200381
Sum274419
Variance52.182421
MonotonicityNot monotonic
2025-02-14T11:12:02.339801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.7 57
 
0.6%
34.5 55
 
0.5%
32.9 53
 
0.5%
24.6 53
 
0.5%
30.1 53
 
0.5%
39.1 52
 
0.5%
17.6 52
 
0.5%
38.6 51
 
0.5%
17.8 51
 
0.5%
24.9 50
 
0.5%
Other values (241) 9473
94.7%
ValueCountFrequency (%)
15 21
0.2%
15.1 28
0.3%
15.2 41
0.4%
15.3 42
0.4%
15.4 34
0.3%
15.5 36
0.4%
15.6 43
0.4%
15.7 42
0.4%
15.8 42
0.4%
15.9 43
0.4%
ValueCountFrequency (%)
40 17
 
0.2%
39.9 37
0.4%
39.8 43
0.4%
39.7 40
0.4%
39.6 35
0.4%
39.5 41
0.4%
39.4 45
0.4%
39.3 36
0.4%
39.2 39
0.4%
39.1 52
0.5%

Stress Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Medium
4092 
Low
2956 
High
2952 

Length

Max length6
Median length4
Mean length4.5228
Min length3

Characters and Unicode

Total characters45228
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowLow
4th rowMedium
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 4092
40.9%
Low 2956
29.6%
High 2952
29.5%

Length

2025-02-14T11:12:02.522700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:02.630117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 4092
40.9%
low 2956
29.6%
high 2952
29.5%

Most occurring characters

ValueCountFrequency (%)
i 7044
15.6%
M 4092
9.0%
e 4092
9.0%
d 4092
9.0%
u 4092
9.0%
m 4092
9.0%
L 2956
6.5%
o 2956
6.5%
w 2956
6.5%
H 2952
6.5%
Other values (2) 5904
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35228
77.9%
Uppercase Letter 10000
 
22.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7044
20.0%
e 4092
11.6%
d 4092
11.6%
u 4092
11.6%
m 4092
11.6%
o 2956
8.4%
w 2956
8.4%
g 2952
8.4%
h 2952
8.4%
Uppercase Letter
ValueCountFrequency (%)
M 4092
40.9%
L 2956
29.6%
H 2952
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 45228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7044
15.6%
M 4092
9.0%
e 4092
9.0%
d 4092
9.0%
u 4092
9.0%
m 4092
9.0%
L 2956
6.5%
o 2956
6.5%
w 2956
6.5%
H 2952
6.5%
Other values (2) 5904
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7044
15.6%
M 4092
9.0%
e 4092
9.0%
d 4092
9.0%
u 4092
9.0%
m 4092
9.0%
L 2956
6.5%
o 2956
6.5%
w 2956
6.5%
H 2952
6.5%
Other values (2) 5904
13.1%
Distinct9892
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:03.028527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.3248
Min length10

Characters and Unicode

Total characters103248
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9786 ?
Unique (%)97.9%

Sample

1st row177.0/63.1
2nd row137.5/110.7
3rd row138.3/76.6
4th row177.1/90.0
5th row130.7/108.8
ValueCountFrequency (%)
166.4/83.7 3
 
< 0.1%
120.0/91.7 3
 
< 0.1%
107.2/111.0 2
 
< 0.1%
153.0/78.6 2
 
< 0.1%
130.1/97.4 2
 
< 0.1%
170.3/116.8 2
 
< 0.1%
173.2/88.2 2
 
< 0.1%
149.6/99.5 2
 
< 0.1%
135.1/66.4 2
 
< 0.1%
160.8/114.4 2
 
< 0.1%
Other values (9882) 9978
99.8%
2025-02-14T11:12:03.513751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20174
19.5%
. 20000
19.4%
/ 10000
9.7%
0 7046
 
6.8%
7 6998
 
6.8%
6 6962
 
6.7%
8 5694
 
5.5%
9 5602
 
5.4%
2 5261
 
5.1%
4 5176
 
5.0%
Other values (2) 10335
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73248
70.9%
Other Punctuation 30000
29.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20174
27.5%
0 7046
 
9.6%
7 6998
 
9.6%
6 6962
 
9.5%
8 5694
 
7.8%
9 5602
 
7.6%
2 5261
 
7.2%
4 5176
 
7.1%
5 5176
 
7.1%
3 5159
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 20000
66.7%
/ 10000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 103248
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20174
19.5%
. 20000
19.4%
/ 10000
9.7%
0 7046
 
6.8%
7 6998
 
6.8%
6 6962
 
6.7%
8 5694
 
5.5%
9 5602
 
5.4%
2 5261
 
5.1%
4 5176
 
5.0%
Other values (2) 10335
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20174
19.5%
. 20000
19.4%
/ 10000
9.7%
0 7046
 
6.8%
7 6998
 
6.8%
6 6962
 
6.7%
8 5694
 
5.5%
9 5602
 
5.4%
2 5261
 
5.1%
4 5176
 
5.0%
Other values (2) 10335
10.0%

Resting Heart Rate (bpm)
Real number (ℝ)

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.4934
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:03.659074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q174
median90
Q3104
95-th percentile117
Maximum119
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.318237
Coefficient of variation (CV)0.19351413
Kurtosis-1.2042462
Mean89.4934
Median Absolute Deviation (MAD)15
Skewness-0.002111082
Sum894934
Variance299.92135
MonotonicityNot monotonic
2025-02-14T11:12:04.108943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 193
 
1.9%
97 193
 
1.9%
111 191
 
1.9%
95 190
 
1.9%
70 187
 
1.9%
117 186
 
1.9%
90 185
 
1.8%
101 184
 
1.8%
74 180
 
1.8%
69 179
 
1.8%
Other values (50) 8132
81.3%
ValueCountFrequency (%)
60 178
1.8%
61 161
1.6%
62 163
1.6%
63 177
1.8%
64 144
1.4%
65 168
1.7%
66 169
1.7%
67 161
1.6%
68 160
1.6%
69 179
1.8%
ValueCountFrequency (%)
119 154
1.5%
118 161
1.6%
117 186
1.9%
116 163
1.6%
115 165
1.7%
114 168
1.7%
113 147
1.5%
112 160
1.6%
111 191
1.9%
110 193
1.9%

ECG Results
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Normal
8516 
Abnormal
1484 

Length

Max length8
Median length6
Mean length6.2968
Min length6

Characters and Unicode

Total characters62968
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 8516
85.2%
Abnormal 1484
 
14.8%

Length

2025-02-14T11:12:04.359700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:04.470655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 8516
85.2%
abnormal 1484
 
14.8%

Most occurring characters

ValueCountFrequency (%)
o 10000
15.9%
r 10000
15.9%
m 10000
15.9%
a 10000
15.9%
l 10000
15.9%
N 8516
13.5%
A 1484
 
2.4%
b 1484
 
2.4%
n 1484
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52968
84.1%
Uppercase Letter 10000
 
15.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10000
18.9%
r 10000
18.9%
m 10000
18.9%
a 10000
18.9%
l 10000
18.9%
b 1484
 
2.8%
n 1484
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
N 8516
85.2%
A 1484
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 62968
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10000
15.9%
r 10000
15.9%
m 10000
15.9%
a 10000
15.9%
l 10000
15.9%
N 8516
13.5%
A 1484
 
2.4%
b 1484
 
2.4%
n 1484
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10000
15.9%
r 10000
15.9%
m 10000
15.9%
a 10000
15.9%
l 10000
15.9%
N 8516
13.5%
A 1484
 
2.4%
b 1484
 
2.4%
n 1484
 
2.4%

Chest Pain Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Typical
2545 
Non-anginal
2508 
Asymptomatic
2487 
Atypical
2460 

Length

Max length12
Median length11
Mean length9.4927
Min length7

Characters and Unicode

Total characters94927
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-anginal
2nd rowNon-anginal
3rd rowTypical
4th rowNon-anginal
5th rowAtypical

Common Values

ValueCountFrequency (%)
Typical 2545
25.4%
Non-anginal 2508
25.1%
Asymptomatic 2487
24.9%
Atypical 2460
24.6%

Length

2025-02-14T11:12:04.593044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T11:12:04.713957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
typical 2545
25.4%
non-anginal 2508
25.1%
asymptomatic 2487
24.9%
atypical 2460
24.6%

Most occurring characters

ValueCountFrequency (%)
a 12508
13.2%
i 10000
10.5%
n 7524
7.9%
l 7513
7.9%
y 7492
7.9%
p 7492
7.9%
c 7492
7.9%
t 7434
7.8%
o 4995
 
5.3%
m 4974
 
5.2%
Other values (6) 17503
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82419
86.8%
Uppercase Letter 10000
 
10.5%
Dash Punctuation 2508
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12508
15.2%
i 10000
12.1%
n 7524
9.1%
l 7513
9.1%
y 7492
9.1%
p 7492
9.1%
c 7492
9.1%
t 7434
9.0%
o 4995
 
6.1%
m 4974
 
6.0%
Other values (2) 4995
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A 4947
49.5%
T 2545
25.4%
N 2508
25.1%
Dash Punctuation
ValueCountFrequency (%)
- 2508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92419
97.4%
Common 2508
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12508
13.5%
i 10000
10.8%
n 7524
8.1%
l 7513
8.1%
y 7492
8.1%
p 7492
8.1%
c 7492
8.1%
t 7434
8.0%
o 4995
 
5.4%
m 4974
 
5.4%
Other values (5) 14995
16.2%
Common
ValueCountFrequency (%)
- 2508
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12508
13.2%
i 10000
10.5%
n 7524
7.9%
l 7513
7.9%
y 7492
7.9%
p 7492
7.9%
c 7492
7.9%
t 7434
7.8%
o 4995
 
5.3%
m 4974
 
5.2%
Other values (6) 17503
18.4%
Distinct121
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.6839
Minimum100
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:05.119100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile105
Q1129
median160
Q3190
95-th percentile215
Maximum220
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.034037
Coefficient of variation (CV)0.21939617
Kurtosis-1.1925333
Mean159.6839
Median Absolute Deviation (MAD)30
Skewness0.0081531526
Sum1596839
Variance1227.3837
MonotonicityNot monotonic
2025-02-14T11:12:05.321898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 107
 
1.1%
104 104
 
1.0%
101 99
 
1.0%
162 98
 
1.0%
180 97
 
1.0%
105 97
 
1.0%
131 96
 
1.0%
109 95
 
0.9%
126 95
 
0.9%
173 95
 
0.9%
Other values (111) 9017
90.2%
ValueCountFrequency (%)
100 81
0.8%
101 99
1.0%
102 80
0.8%
103 87
0.9%
104 104
1.0%
105 97
1.0%
106 83
0.8%
107 74
0.7%
108 80
0.8%
109 95
0.9%
ValueCountFrequency (%)
220 85
0.9%
219 92
0.9%
218 83
0.8%
217 90
0.9%
216 75
0.8%
215 91
0.9%
214 95
0.9%
213 76
0.8%
212 88
0.9%
211 68
0.7%

Exercise Induced Angina
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9011 
True
989 
ValueCountFrequency (%)
False 9011
90.1%
True 989
 
9.9%
2025-02-14T11:12:05.456267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.94073
Minimum90
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:05.600138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90.5
Q192.4
median94.9
Q397.4
95-th percentile99.5
Maximum100
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8833289
Coefficient of variation (CV)0.030369778
Kurtosis-1.1911426
Mean94.94073
Median Absolute Deviation (MAD)2.5
Skewness0.025636142
Sum949407.3
Variance8.3135854
MonotonicityNot monotonic
2025-02-14T11:12:05.813365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.1 130
 
1.3%
95.1 117
 
1.2%
92.2 116
 
1.2%
95.2 116
 
1.2%
91.5 115
 
1.1%
96 115
 
1.1%
90.6 115
 
1.1%
93.7 113
 
1.1%
92.3 112
 
1.1%
94.1 112
 
1.1%
Other values (91) 8839
88.4%
ValueCountFrequency (%)
90 55
0.5%
90.1 112
1.1%
90.2 84
0.8%
90.3 108
1.1%
90.4 107
1.1%
90.5 96
1.0%
90.6 115
1.1%
90.7 106
1.1%
90.8 104
1.0%
90.9 103
1.0%
ValueCountFrequency (%)
100 51
0.5%
99.9 85
0.9%
99.8 102
1.0%
99.7 100
1.0%
99.6 101
1.0%
99.5 102
1.0%
99.4 90
0.9%
99.3 103
1.0%
99.2 111
1.1%
99.1 84
0.8%
Distinct451
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.0101
Minimum50
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-14T11:12:06.023357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile72
Q1164
median277
Q3385
95-th percentile477
Maximum500
Range450
Interquartile range (IQR)221

Descriptive statistics

Standard deviation128.82599
Coefficient of variation (CV)0.46844095
Kurtosis-1.17229
Mean275.0101
Median Absolute Deviation (MAD)110.5
Skewness-0.011710495
Sum2750101
Variance16596.137
MonotonicityNot monotonic
2025-02-14T11:12:06.683042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388 38
 
0.4%
308 34
 
0.3%
280 34
 
0.3%
293 33
 
0.3%
427 32
 
0.3%
228 32
 
0.3%
93 32
 
0.3%
409 32
 
0.3%
60 32
 
0.3%
377 32
 
0.3%
Other values (441) 9669
96.7%
ValueCountFrequency (%)
50 20
0.2%
51 19
0.2%
52 19
0.2%
53 16
0.2%
54 16
0.2%
55 25
0.2%
56 19
0.2%
57 17
0.2%
58 30
0.3%
59 25
0.2%
ValueCountFrequency (%)
500 19
0.2%
499 22
0.2%
498 25
0.2%
497 19
0.2%
496 13
0.1%
495 20
0.2%
494 27
0.3%
493 23
0.2%
492 20
0.2%
491 20
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7962 
True
2038 
ValueCountFrequency (%)
False 7962
79.6%
True 2038
 
20.4%
2025-02-14T11:12:06.821523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-02-14T11:11:53.996772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:38.836002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:41.371442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:43.880075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:45.504646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:47.573278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:49.150409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.734715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:52.302621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:54.291842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:39.024125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:41.628441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.066547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:45.695974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:47.749408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:49.320335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.900865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:52.545112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:54.593207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:39.299577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:41.853958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.238986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:45.875111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:47.917055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:49.529726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.068955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:52.720534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:54.886702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:39.606251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:42.127508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.433284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:46.092273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.111955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:49.706934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.263619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:52.896210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:55.121686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:39.907081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:42.458803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.616750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:46.288343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.278902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:49.883347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.447752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:53.068711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:55.419960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:40.195669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:42.760779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.781735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:46.526438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.481533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.061273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.607907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:53.240934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:55.714998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:40.460257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:43.055200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:44.971926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:46.711780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.649431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.238321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.780580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:53.446176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:56.456331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:40.760181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:43.359434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:45.142845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:47.213950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.812184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.413461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:51.942038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:53.613312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:56.699333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:41.036973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:43.655102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:45.310583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:47.402081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:48.967991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:50.575852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:52.112677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T11:11:53.801433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-14T11:12:06.963350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAlcohol ConsumptionBMI (kg/m²)Blood Oxygen Levels (SpO2%)Chest Pain TypeCholesterol Levels (mg/dL)DiabetesDiet TypeECG ResultsExercise Induced AnginaFamily History of Heart DiseaseGenderHeart Attack LikelihoodHypertensionMaximum Heart Rate AchievedPhysical Activity LevelRegionResting Heart Rate (bpm)SESScreen Time (hrs/day)Sleep Duration (hrs/day)Smoking StatusStress LevelTriglyceride Levels (mg/dL)Urban/Rural
Age1.0000.000-0.0030.0080.000-0.0160.0000.0000.0000.0370.0000.0000.0000.000-0.0000.0160.0000.0020.007-0.006-0.0070.0000.0230.0060.000
Alcohol Consumption0.0001.0000.0000.0000.0260.0140.0000.0060.0000.0060.0000.0000.0000.0000.0180.0050.0090.0120.0000.0130.0000.0000.0000.0040.000
BMI (kg/m²)-0.0030.0001.0000.0010.0000.0160.0000.0000.0000.0000.0000.0130.0210.0000.0230.0130.007-0.0090.0000.001-0.0060.0150.0000.0030.000
Blood Oxygen Levels (SpO2%)0.0080.0000.0011.0000.018-0.0000.0000.0000.0240.0140.0000.0000.0000.0160.0130.0000.000-0.0010.0000.0090.0090.0150.0060.0090.000
Chest Pain Type0.0000.0260.0000.0181.0000.0240.0000.0000.0130.0000.0080.0000.0000.0100.0030.0170.0070.0080.0000.0000.0000.0000.0000.0000.014
Cholesterol Levels (mg/dL)-0.0160.0140.016-0.0000.0241.0000.0080.0220.0000.0090.0000.0040.0210.0000.0080.0210.015-0.0120.0000.0270.0180.0000.014-0.0010.012
Diabetes0.0000.0000.0000.0000.0000.0081.0000.0000.0090.0000.0000.0000.0000.0000.0120.0170.0130.0000.0000.0090.0440.0000.0080.0330.000
Diet Type0.0000.0060.0000.0000.0000.0220.0001.0000.0000.0000.0210.0090.0000.0200.0000.0120.0000.0000.0160.0070.0000.0190.0060.0120.000
ECG Results0.0000.0000.0000.0240.0130.0000.0090.0001.0000.0000.0000.0030.0000.0000.0100.0000.0070.0000.0170.0330.0000.0000.0160.0110.002
Exercise Induced Angina0.0370.0060.0000.0140.0000.0090.0000.0000.0001.0000.0080.0000.0000.0000.0200.0000.0000.0050.0000.0000.0000.0000.0110.0210.000
Family History of Heart Disease0.0000.0000.0000.0000.0080.0000.0000.0210.0000.0081.0000.0000.0000.0000.0000.0260.0000.0180.0000.0000.0000.0140.0000.0180.010
Gender0.0000.0000.0130.0000.0000.0040.0000.0090.0030.0000.0001.0000.0180.0000.0000.0110.0000.0000.0000.0180.0060.0070.0040.0000.019
Heart Attack Likelihood0.0000.0000.0210.0000.0000.0210.0000.0000.0000.0000.0000.0181.0000.0000.0000.0120.0120.0130.0000.0000.0000.0000.0300.0000.017
Hypertension0.0000.0000.0000.0160.0100.0000.0000.0200.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0050.0120.0120.0000.0000.0000.000
Maximum Heart Rate Achieved-0.0000.0180.0230.0130.0030.0080.0120.0000.0100.0200.0000.0000.0000.0001.0000.0120.012-0.0150.0000.013-0.0010.0120.0000.0090.024
Physical Activity Level0.0160.0050.0130.0000.0170.0210.0170.0120.0000.0000.0260.0110.0120.0000.0121.0000.0000.0000.0000.0000.0000.0040.0000.0170.000
Region0.0000.0090.0070.0000.0070.0150.0130.0000.0070.0000.0000.0000.0120.0000.0120.0001.0000.0000.0000.0000.0000.0000.0000.0140.000
Resting Heart Rate (bpm)0.0020.012-0.009-0.0010.008-0.0120.0000.0000.0000.0050.0180.0000.0130.000-0.0150.0000.0001.0000.0000.020-0.0060.0000.000-0.0040.000
SES0.0070.0000.0000.0000.0000.0000.0000.0160.0170.0000.0000.0000.0000.0050.0000.0000.0000.0001.0000.0030.0130.0000.0000.0000.000
Screen Time (hrs/day)-0.0060.0130.0010.0090.0000.0270.0090.0070.0330.0000.0000.0180.0000.0120.0130.0000.0000.0200.0031.000-0.0040.0000.000-0.0100.000
Sleep Duration (hrs/day)-0.0070.000-0.0060.0090.0000.0180.0440.0000.0000.0000.0000.0060.0000.012-0.0010.0000.000-0.0060.013-0.0041.0000.0000.000-0.0020.000
Smoking Status0.0000.0000.0150.0150.0000.0000.0000.0190.0000.0000.0140.0070.0000.0000.0120.0040.0000.0000.0000.0000.0001.0000.0000.0000.000
Stress Level0.0230.0000.0000.0060.0000.0140.0080.0060.0160.0110.0000.0040.0300.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0090.000
Triglyceride Levels (mg/dL)0.0060.0040.0030.0090.000-0.0010.0330.0120.0110.0210.0180.0000.0000.0000.0090.0170.014-0.0040.000-0.010-0.0020.0000.0091.0000.010
Urban/Rural0.0000.0000.0000.0000.0140.0120.0000.0000.0020.0000.0100.0190.0170.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0101.000

Missing values

2025-02-14T11:11:57.135324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T11:11:57.629959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderRegionUrban/RuralSESSmoking StatusAlcohol ConsumptionDiet TypePhysical Activity LevelScreen Time (hrs/day)Sleep Duration (hrs/day)Family History of Heart DiseaseDiabetesHypertensionCholesterol Levels (mg/dL)BMI (kg/m²)Stress LevelBlood Pressure (systolic/diastolic mmHg)Resting Heart Rate (bpm)ECG ResultsChest Pain TypeMaximum Heart Rate AchievedExercise Induced AnginaBlood Oxygen Levels (SpO2%)Triglyceride Levels (mg/dL)Heart Attack Likelihood
030MaleEastUrbanMiddleNeverRegularlyNon-VegetarianSedentary38NoNoYes14834.4High177.0/63.182NormalNon-anginal183No94.158No
124FemaleEastUrbanLowOccasionallyOccasionallyNon-VegetarianSedentary159NoNoNo12425.0High137.5/110.776NormalNon-anginal118No97.1341No
224FemaleNorthUrbanLowOccasionallyOccasionallyVeganHigh153YesYesNo25633.9Low138.3/76.686NormalTypical164No92.7373Yes
327MaleEastUrbanMiddleOccasionallyNeverVegetarianSedentary67NoNoNo13719.0Medium177.1/90.0106NormalNon-anginal188No98.4102Yes
421FemaleWestRuralLowOccasionallyOccasionallyVegetarianModerate49YesNoNo26228.0Low130.7/108.873NormalAtypical216No94.9235No
520MaleWestRuralMiddleNeverNeverNon-VegetarianHigh25YesNoNo20515.5High171.5/107.1115NormalAtypical142No93.0129No
629MaleEastRuralHighRegularlyNeverNon-VegetarianModerate810YesYesYes27821.4Low176.7/110.0118NormalNon-anginal181No93.4444No
732FemaleNorthUrbanLowNeverOccasionallyNon-VegetarianSedentary134NoNoNo25417.9High146.2/76.671NormalAtypical210No95.0316No
819FemaleWestRuralMiddleOccasionallyOccasionallyNon-VegetarianSedentary39YesNoNo13218.2Medium132.3/98.970NormalNon-anginal161No90.9241No
935MaleWestUrbanHighOccasionallyNeverNon-VegetarianSedentary129YesNoYes26830.7High140.5/106.1110NormalAsymptomatic141No97.1297No
AgeGenderRegionUrban/RuralSESSmoking StatusAlcohol ConsumptionDiet TypePhysical Activity LevelScreen Time (hrs/day)Sleep Duration (hrs/day)Family History of Heart DiseaseDiabetesHypertensionCholesterol Levels (mg/dL)BMI (kg/m²)Stress LevelBlood Pressure (systolic/diastolic mmHg)Resting Heart Rate (bpm)ECG ResultsChest Pain TypeMaximum Heart Rate AchievedExercise Induced AnginaBlood Oxygen Levels (SpO2%)Triglyceride Levels (mg/dL)Heart Attack Likelihood
999026FemaleNorthUrbanLowRegularlyNeverVegetarianSedentary119YesNoNo25233.0High148.8/67.4115AbnormalAtypical198No92.7409No
999120FemaleSouthRuralLowNeverNeverVegetarianModerate57NoNoNo20926.0Medium171.0/106.797AbnormalAtypical189No93.9322No
999228FemaleEastRuralMiddleOccasionallyOccasionallyVegetarianModerate27NoNoNo12119.2Low117.2/80.390AbnormalAtypical108No94.5466No
999330FemaleEastRuralLowNeverRegularlyVegetarianModerate1010NoNoYes29637.2High167.5/106.097NormalAsymptomatic125No90.2173No
999420FemaleWestRuralMiddleOccasionallyOccasionallyNon-VegetarianSedentary154NoNoNo15331.0Medium122.0/73.596NormalNon-anginal159No95.8132No
999533FemaleEastRuralLowOccasionallyOccasionallyVeganSedentary24NoYesNo14120.1Low127.6/64.888AbnormalAtypical147No90.8296No
999635FemaleNorth-EastRuralLowOccasionallyNeverVeganHigh147NoNoNo18827.4Low162.5/77.4116NormalAtypical136No95.7254No
999732MaleWestUrbanMiddleRegularlyNeverNon-VegetarianModerate15YesYesNo22122.9High120.8/81.362NormalTypical171No98.6319No
999821FemaleCentralRuralMiddleRegularlyNeverVeganSedentary94YesNoNo18835.1Medium110.0/100.384AbnormalNon-anginal137No91.5317No
999928MaleNorthRuralHighNeverNeverVegetarianSedentary53NoNoYes12933.1High166.0/83.577NormalAsymptomatic220No97.480No